Overview

Dataset statistics

Number of variables27
Number of observations205
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.4 KiB
Average record size in memory216.6 B

Variable types

Numeric17
Categorical10

Alerts

modele has a high cardinality: 141 distinct valuesHigh cardinality
car_ID is highly overall correlated with marqueHigh correlation
etat_de_route is highly overall correlated with empattement and 2 other fieldsHigh correlation
empattement is highly overall correlated with etat_de_route and 10 other fieldsHigh correlation
longueur_voiture is highly overall correlated with empattement and 9 other fieldsHigh correlation
largeur_voiture is highly overall correlated with empattement and 9 other fieldsHigh correlation
hauteur_voiture is highly overall correlated with etat_de_route and 3 other fieldsHigh correlation
poids_vehicule is highly overall correlated with empattement and 9 other fieldsHigh correlation
nombre_cylindres is highly overall correlated with poids_vehicule and 7 other fieldsHigh correlation
taille_moteur is highly overall correlated with empattement and 12 other fieldsHigh correlation
taux_alésage is highly overall correlated with empattement and 9 other fieldsHigh correlation
course is highly overall correlated with emplacement_moteur and 1 other fieldsHigh correlation
taux_compression is highly overall correlated with carburant and 2 other fieldsHigh correlation
chevaux is highly overall correlated with empattement and 11 other fieldsHigh correlation
tour_moteur is highly overall correlated with carburantHigh correlation
consommation_ville is highly overall correlated with longueur_voiture and 8 other fieldsHigh correlation
consommation_autoroute is highly overall correlated with empattement and 9 other fieldsHigh correlation
prix is highly overall correlated with empattement and 9 other fieldsHigh correlation
carburant is highly overall correlated with taux_compression and 2 other fieldsHigh correlation
turbo is highly overall correlated with taux_compression and 1 other fieldsHigh correlation
nombre_portes is highly overall correlated with etat_de_route and 2 other fieldsHigh correlation
type_vehicule is highly overall correlated with nombre_portesHigh correlation
roues_motrices is highly overall correlated with marqueHigh correlation
emplacement_moteur is highly overall correlated with empattement and 4 other fieldsHigh correlation
type_moteur is highly overall correlated with nombre_cylindres and 3 other fieldsHigh correlation
systeme_carburant is highly overall correlated with taux_compression and 3 other fieldsHigh correlation
marque is highly overall correlated with car_ID and 9 other fieldsHigh correlation
carburant is highly imbalanced (53.9%)Imbalance
emplacement_moteur is highly imbalanced (89.0%)Imbalance
car_ID is uniformly distributedUniform
modele is uniformly distributedUniform
car_ID has unique valuesUnique
etat_de_route has 67 (32.7%) zerosZeros

Reproduction

Analysis started2023-04-25 12:08:03.095934
Analysis finished2023-04-25 12:08:54.601599
Duration51.51 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

car_ID
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct205
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103
Minimum1
Maximum205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T14:08:54.718861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.2
Q152
median103
Q3154
95-th percentile194.8
Maximum205
Range204
Interquartile range (IQR)102

Descriptive statistics

Standard deviation59.322565
Coefficient of variation (CV)0.57594723
Kurtosis-1.2
Mean103
Median Absolute Deviation (MAD)51
Skewness0
Sum21115
Variance3519.1667
MonotonicityStrictly increasing
2023-04-25T14:08:54.907327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
142 1
 
0.5%
132 1
 
0.5%
133 1
 
0.5%
134 1
 
0.5%
135 1
 
0.5%
136 1
 
0.5%
137 1
 
0.5%
138 1
 
0.5%
139 1
 
0.5%
Other values (195) 195
95.1%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
205 1
0.5%
204 1
0.5%
203 1
0.5%
202 1
0.5%
201 1
0.5%
200 1
0.5%
199 1
0.5%
198 1
0.5%
197 1
0.5%
196 1
0.5%

etat_de_route
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83414634
Minimum-2
Maximum3
Zeros67
Zeros (%)32.7%
Negative25
Negative (%)12.2%
Memory size1.7 KiB
2023-04-25T14:08:55.336622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2453068
Coefficient of variation (CV)1.4929117
Kurtosis-0.67627136
Mean0.83414634
Median Absolute Deviation (MAD)1
Skewness0.21107227
Sum171
Variance1.5507891
MonotonicityNot monotonic
2023-04-25T14:08:55.476171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 67
32.7%
1 54
26.3%
2 32
15.6%
3 27
13.2%
-1 22
 
10.7%
-2 3
 
1.5%
ValueCountFrequency (%)
-2 3
 
1.5%
-1 22
 
10.7%
0 67
32.7%
1 54
26.3%
2 32
15.6%
3 27
13.2%
ValueCountFrequency (%)
3 27
13.2%
2 32
15.6%
1 54
26.3%
0 67
32.7%
-1 22
 
10.7%
-2 3
 
1.5%

carburant
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
gas
185 
diesel
20 

Length

Max length6
Median length3
Mean length3.2926829
Min length3

Characters and Unicode

Total characters675
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgas
2nd rowgas
3rd rowgas
4th rowgas
5th rowgas

Common Values

ValueCountFrequency (%)
gas 185
90.2%
diesel 20
 
9.8%

Length

2023-04-25T14:08:55.622050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T14:08:55.789633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
gas 185
90.2%
diesel 20
 
9.8%

Most occurring characters

ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 675
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 675
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

turbo
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
standard
168 
turbo
37 

Length

Max length8
Median length8
Mean length7.4585366
Min length5

Characters and Unicode

Total characters1529
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstandard
2nd rowstandard
3rd rowstandard
4th rowstandard
5th rowstandard

Common Values

ValueCountFrequency (%)
standard 168
82.0%
turbo 37
 
18.0%

Length

2023-04-25T14:08:55.917445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T14:08:56.071984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
standard 168
82.0%
turbo 37
 
18.0%

Most occurring characters

ValueCountFrequency (%)
a 336
22.0%
d 336
22.0%
t 205
13.4%
r 205
13.4%
s 168
11.0%
n 168
11.0%
u 37
 
2.4%
b 37
 
2.4%
o 37
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1529
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 336
22.0%
d 336
22.0%
t 205
13.4%
r 205
13.4%
s 168
11.0%
n 168
11.0%
u 37
 
2.4%
b 37
 
2.4%
o 37
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 1529
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 336
22.0%
d 336
22.0%
t 205
13.4%
r 205
13.4%
s 168
11.0%
n 168
11.0%
u 37
 
2.4%
b 37
 
2.4%
o 37
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1529
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 336
22.0%
d 336
22.0%
t 205
13.4%
r 205
13.4%
s 168
11.0%
n 168
11.0%
u 37
 
2.4%
b 37
 
2.4%
o 37
 
2.4%

nombre_portes
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
4
115 
2
90 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters205
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 115
56.1%
2 90
43.9%

Length

2023-04-25T14:08:56.219435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T14:08:56.355429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
4 115
56.1%
2 90
43.9%

Most occurring characters

ValueCountFrequency (%)
4 115
56.1%
2 90
43.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 205
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 115
56.1%
2 90
43.9%

Most occurring scripts

ValueCountFrequency (%)
Common 205
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 115
56.1%
2 90
43.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 205
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 115
56.1%
2 90
43.9%

type_vehicule
Categorical

Distinct5
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
berline
96 
hayon
70 
break
25 
coupe
 
8
decapotable
 
6

Length

Max length11
Median length5
Mean length6.1121951
Min length5

Characters and Unicode

Total characters1253
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdecapotable
2nd rowdecapotable
3rd rowhayon
4th rowberline
5th rowberline

Common Values

ValueCountFrequency (%)
berline 96
46.8%
hayon 70
34.1%
break 25
 
12.2%
coupe 8
 
3.9%
decapotable 6
 
2.9%

Length

2023-04-25T14:08:56.477437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T14:08:56.638298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
berline 96
46.8%
hayon 70
34.1%
break 25
 
12.2%
coupe 8
 
3.9%
decapotable 6
 
2.9%

Most occurring characters

ValueCountFrequency (%)
e 237
18.9%
n 166
13.2%
b 127
10.1%
r 121
9.7%
a 107
8.5%
l 102
8.1%
i 96
7.7%
o 84
 
6.7%
h 70
 
5.6%
y 70
 
5.6%
Other values (6) 73
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1253
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 237
18.9%
n 166
13.2%
b 127
10.1%
r 121
9.7%
a 107
8.5%
l 102
8.1%
i 96
7.7%
o 84
 
6.7%
h 70
 
5.6%
y 70
 
5.6%
Other values (6) 73
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 1253
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 237
18.9%
n 166
13.2%
b 127
10.1%
r 121
9.7%
a 107
8.5%
l 102
8.1%
i 96
7.7%
o 84
 
6.7%
h 70
 
5.6%
y 70
 
5.6%
Other values (6) 73
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1253
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 237
18.9%
n 166
13.2%
b 127
10.1%
r 121
9.7%
a 107
8.5%
l 102
8.1%
i 96
7.7%
o 84
 
6.7%
h 70
 
5.6%
y 70
 
5.6%
Other values (6) 73
 
5.8%

roues_motrices
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
avant
120 
arriere
76 
4motrice
 
9

Length

Max length8
Median length5
Mean length5.8731707
Min length5

Characters and Unicode

Total characters1204
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowarriere
2nd rowarriere
3rd rowarriere
4th rowavant
5th row4motrice

Common Values

ValueCountFrequency (%)
avant 120
58.5%
arriere 76
37.1%
4motrice 9
 
4.4%

Length

2023-04-25T14:08:56.791733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T14:08:57.004919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
avant 120
58.5%
arriere 76
37.1%
4motrice 9
 
4.4%

Most occurring characters

ValueCountFrequency (%)
a 316
26.2%
r 237
19.7%
e 161
13.4%
t 129
10.7%
v 120
 
10.0%
n 120
 
10.0%
i 85
 
7.1%
4 9
 
0.7%
m 9
 
0.7%
o 9
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1195
99.3%
Decimal Number 9
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 316
26.4%
r 237
19.8%
e 161
13.5%
t 129
10.8%
v 120
 
10.0%
n 120
 
10.0%
i 85
 
7.1%
m 9
 
0.8%
o 9
 
0.8%
c 9
 
0.8%
Decimal Number
ValueCountFrequency (%)
4 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1195
99.3%
Common 9
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 316
26.4%
r 237
19.8%
e 161
13.5%
t 129
10.8%
v 120
 
10.0%
n 120
 
10.0%
i 85
 
7.1%
m 9
 
0.8%
o 9
 
0.8%
c 9
 
0.8%
Common
ValueCountFrequency (%)
4 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1204
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 316
26.2%
r 237
19.7%
e 161
13.4%
t 129
10.7%
v 120
 
10.0%
n 120
 
10.0%
i 85
 
7.1%
4 9
 
0.7%
m 9
 
0.7%
o 9
 
0.7%

emplacement_moteur
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
devant
202 
derrier
 
3

Length

Max length7
Median length6
Mean length6.0146341
Min length6

Characters and Unicode

Total characters1233
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdevant
2nd rowdevant
3rd rowdevant
4th rowdevant
5th rowdevant

Common Values

ValueCountFrequency (%)
devant 202
98.5%
derrier 3
 
1.5%

Length

2023-04-25T14:08:57.142581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T14:08:57.317576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
devant 202
98.5%
derrier 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
e 208
16.9%
d 205
16.6%
v 202
16.4%
a 202
16.4%
n 202
16.4%
t 202
16.4%
r 9
 
0.7%
i 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1233
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 208
16.9%
d 205
16.6%
v 202
16.4%
a 202
16.4%
n 202
16.4%
t 202
16.4%
r 9
 
0.7%
i 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 1233
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 208
16.9%
d 205
16.6%
v 202
16.4%
a 202
16.4%
n 202
16.4%
t 202
16.4%
r 9
 
0.7%
i 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1233
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 208
16.9%
d 205
16.6%
v 202
16.4%
a 202
16.4%
n 202
16.4%
t 202
16.4%
r 9
 
0.7%
i 3
 
0.2%

empattement
Real number (ℝ)

Distinct37
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5080976
Minimum2.2
Maximum3.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T14:08:57.492158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile2.36
Q12.4
median2.46
Q32.6
95-th percentile2.79
Maximum3.07
Range0.87
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.15279356
Coefficient of variation (CV)0.060920103
Kurtosis1.0320752
Mean2.5080976
Median Absolute Deviation (MAD)0.07
Skewness1.0494166
Sum514.16
Variance0.023345873
MonotonicityNot monotonic
2023-04-25T14:08:57.652026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
2.4 22
 
10.7%
2.38 20
 
9.8%
2.45 15
 
7.3%
2.43 13
 
6.3%
2.47 12
 
5.9%
2.44 8
 
3.9%
2.52 8
 
3.9%
2.65 8
 
3.9%
2.5 7
 
3.4%
2.74 7
 
3.4%
Other values (27) 85
41.5%
ValueCountFrequency (%)
2.2 2
 
1.0%
2.25 3
 
1.5%
2.27 3
 
1.5%
2.32 2
 
1.0%
2.36 6
 
2.9%
2.37 1
 
0.5%
2.38 20
9.8%
2.4 22
10.7%
2.42 5
 
2.4%
2.43 13
6.3%
ValueCountFrequency (%)
3.07 1
 
0.5%
2.94 2
 
1.0%
2.9 4
2.0%
2.87 2
 
1.0%
2.84 1
 
0.5%
2.79 3
1.5%
2.77 5
2.4%
2.74 7
3.4%
2.71 1
 
0.5%
2.69 3
1.5%

longueur_voiture
Real number (ℝ)

Distinct58
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4213659
Minimum3.58
Maximum5.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T14:08:57.874002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3.58
5-th percentile3.992
Q14.22
median4.4
Q34.65
95-th percentile4.984
Maximum5.29
Range1.71
Interquartile range (IQR)0.43

Descriptive statistics

Standard deviation0.31339083
Coefficient of variation (CV)0.070880999
Kurtosis-0.077272115
Mean4.4213659
Median Absolute Deviation (MAD)0.18
Skewness0.15609359
Sum906.38
Variance0.098213812
MonotonicityNot monotonic
2023-04-25T14:08:58.084354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 15
 
7.3%
4.8 13
 
6.3%
4.74 13
 
6.3%
4.29 10
 
4.9%
4.46 9
 
4.4%
4.36 7
 
3.4%
4.49 7
 
3.4%
4.4 7
 
3.4%
4.22 7
 
3.4%
4.52 6
 
2.9%
Other values (48) 111
54.1%
ValueCountFrequency (%)
3.58 1
 
0.5%
3.67 2
 
1.0%
3.81 3
 
1.5%
3.96 3
 
1.5%
3.99 2
 
1.0%
4 15
7.3%
4.01 1
 
0.5%
4.03 4
 
2.0%
4.04 3
 
1.5%
4.05 1
 
0.5%
ValueCountFrequency (%)
5.29 1
 
0.5%
5.15 2
1.0%
5.07 2
1.0%
5.06 1
 
0.5%
5.05 4
2.0%
5 1
 
0.5%
4.92 1
 
0.5%
4.89 3
1.5%
4.87 1
 
0.5%
4.85 2
1.0%

largeur_voiture
Real number (ℝ)

Distinct25
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6748293
Minimum1.53
Maximum1.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T14:08:58.251894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.53
5-th percentile1.62
Q11.63
median1.66
Q31.7
95-th percentile1.79
Maximum1.84
Range0.31
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.05412943
Coefficient of variation (CV)0.032319372
Kurtosis0.73104136
Mean1.6748293
Median Absolute Deviation (MAD)0.03
Skewness0.90647936
Sum343.34
Variance0.0029299952
MonotonicityNot monotonic
2023-04-25T14:08:58.405039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1.62 38
18.5%
1.66 30
14.6%
1.69 25
12.2%
1.63 17
8.3%
1.64 12
 
5.9%
1.68 10
 
4.9%
1.74 10
 
4.9%
1.67 10
 
4.9%
1.72 7
 
3.4%
1.65 7
 
3.4%
Other values (15) 39
19.0%
ValueCountFrequency (%)
1.53 1
 
0.5%
1.57 1
 
0.5%
1.59 1
 
0.5%
1.61 1
 
0.5%
1.62 38
18.5%
1.63 17
8.3%
1.64 12
 
5.9%
1.65 7
 
3.4%
1.66 30
14.6%
1.67 10
 
4.9%
ValueCountFrequency (%)
1.84 1
 
0.5%
1.83 1
 
0.5%
1.82 3
 
1.5%
1.81 3
 
1.5%
1.8 1
 
0.5%
1.79 5
2.4%
1.77 2
 
1.0%
1.75 5
2.4%
1.74 10
4.9%
1.73 3
 
1.5%

hauteur_voiture
Real number (ℝ)

Distinct26
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3638049
Minimum1.21
Maximum1.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T14:08:58.611535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.21
5-th percentile1.26
Q11.32
median1.37
Q31.41
95-th percentile1.46
Maximum1.52
Range0.31
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.061414383
Coefficient of variation (CV)0.045031649
Kurtosis-0.34699726
Mean1.3638049
Median Absolute Deviation (MAD)0.04
Skewness0.08890014
Sum279.58
Variance0.0037717264
MonotonicityNot monotonic
2023-04-25T14:08:58.747497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1.41 23
 
11.2%
1.38 20
 
9.8%
1.29 19
 
9.3%
1.34 13
 
6.3%
1.35 12
 
5.9%
1.37 12
 
5.9%
1.32 12
 
5.9%
1.44 10
 
4.9%
1.39 9
 
4.4%
1.42 9
 
4.4%
Other values (16) 66
32.2%
ValueCountFrequency (%)
1.21 1
 
0.5%
1.24 2
 
1.0%
1.25 2
 
1.0%
1.26 7
 
3.4%
1.28 8
3.9%
1.29 19
9.3%
1.3 1
 
0.5%
1.31 9
4.4%
1.32 12
5.9%
1.33 4
 
2.0%
ValueCountFrequency (%)
1.52 2
 
1.0%
1.5 3
 
1.5%
1.49 4
 
2.0%
1.48 1
 
0.5%
1.46 3
 
1.5%
1.44 10
4.9%
1.43 5
 
2.4%
1.42 9
 
4.4%
1.41 23
11.2%
1.4 6
 
2.9%

poids_vehicule
Real number (ℝ)

Distinct79
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2777561
Minimum0.74
Maximum2.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T14:08:58.918241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.74
5-th percentile0.95
Q11.07
median1.21
Q31.47
95-th percentile1.75
Maximum2.03
Range1.29
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.26099891
Coefficient of variation (CV)0.20426348
Kurtosis-0.063173862
Mean1.2777561
Median Absolute Deviation (MAD)0.19
Skewness0.67700729
Sum261.94
Variance0.06812043
MonotonicityNot monotonic
2023-04-25T14:08:59.157406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.19 7
 
3.4%
1.14 7
 
3.4%
1.2 7
 
3.4%
1.27 6
 
2.9%
0.94 6
 
2.9%
1.01 5
 
2.4%
1.1 5
 
2.4%
0.99 5
 
2.4%
0.98 5
 
2.4%
1.15 5
 
2.4%
Other values (69) 147
71.7%
ValueCountFrequency (%)
0.74 1
 
0.5%
0.86 1
 
0.5%
0.91 1
 
0.5%
0.92 1
 
0.5%
0.94 6
2.9%
0.95 4
2.0%
0.96 3
1.5%
0.97 4
2.0%
0.98 5
2.4%
0.99 5
2.4%
ValueCountFrequency (%)
2.03 2
1.0%
1.98 1
0.5%
1.95 1
0.5%
1.88 2
1.0%
1.87 1
0.5%
1.86 1
0.5%
1.84 1
0.5%
1.76 1
0.5%
1.75 2
1.0%
1.74 1
0.5%

type_moteur
Categorical

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
ACT
148 
soupapes en tête à flux croisés
15 
soupapes en tête à manchon
 
13
double ACT
 
12
ligne des cylindres
 
12
Other values (2)
 
5

Length

Max length31
Median length3
Mean length8.1804878
Min length3

Characters and Unicode

Total characters1677
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowdouble ACT
2nd rowdouble ACT
3rd rowsoupapes en tête à manchon
4th rowACT
5th rowACT

Common Values

ValueCountFrequency (%)
ACT 148
72.2%
soupapes en tête à flux croisés 15
 
7.3%
soupapes en tête à manchon 13
 
6.3%
double ACT 12
 
5.9%
ligne des cylindres 12
 
5.9%
moteur rotatif 4
 
2.0%
double ACT à soupapes en V 1
 
0.5%

Length

2023-04-25T14:08:59.336898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T14:08:59.581655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
act 161
42.7%
soupapes 29
 
7.7%
en 29
 
7.7%
à 29
 
7.7%
tête 28
 
7.4%
flux 15
 
4.0%
croisés 15
 
4.0%
manchon 13
 
3.4%
double 13
 
3.4%
ligne 12
 
3.2%
Other values (5) 33
 
8.8%

Most occurring characters

ValueCountFrequency (%)
172
 
10.3%
C 161
 
9.6%
A 161
 
9.6%
T 161
 
9.6%
e 139
 
8.3%
s 112
 
6.7%
n 79
 
4.7%
o 78
 
4.7%
t 68
 
4.1%
u 61
 
3.6%
Other values (18) 485
28.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1021
60.9%
Uppercase Letter 484
28.9%
Space Separator 172
 
10.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 139
13.6%
s 112
 
11.0%
n 79
 
7.7%
o 78
 
7.6%
t 68
 
6.7%
u 61
 
6.0%
p 58
 
5.7%
l 52
 
5.1%
a 46
 
4.5%
i 43
 
4.2%
Other values (13) 285
27.9%
Uppercase Letter
ValueCountFrequency (%)
C 161
33.3%
A 161
33.3%
T 161
33.3%
V 1
 
0.2%
Space Separator
ValueCountFrequency (%)
172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1505
89.7%
Common 172
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 161
 
10.7%
A 161
 
10.7%
T 161
 
10.7%
e 139
 
9.2%
s 112
 
7.4%
n 79
 
5.2%
o 78
 
5.2%
t 68
 
4.5%
u 61
 
4.1%
p 58
 
3.9%
Other values (17) 427
28.4%
Common
ValueCountFrequency (%)
172
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1605
95.7%
None 72
 
4.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
172
10.7%
C 161
 
10.0%
A 161
 
10.0%
T 161
 
10.0%
e 139
 
8.7%
s 112
 
7.0%
n 79
 
4.9%
o 78
 
4.9%
t 68
 
4.2%
u 61
 
3.8%
Other values (15) 413
25.7%
None
ValueCountFrequency (%)
à 29
40.3%
ê 28
38.9%
é 15
20.8%

nombre_cylindres
Real number (ℝ)

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3804878
Minimum2
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T14:08:59.738594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q14
median4
Q34
95-th percentile6
Maximum12
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0808538
Coefficient of variation (CV)0.24674279
Kurtosis13.714866
Mean4.3804878
Median Absolute Deviation (MAD)0
Skewness2.817459
Sum898
Variance1.1682449
MonotonicityNot monotonic
2023-04-25T14:08:59.945999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 159
77.6%
6 24
 
11.7%
5 11
 
5.4%
8 5
 
2.4%
2 4
 
2.0%
3 1
 
0.5%
12 1
 
0.5%
ValueCountFrequency (%)
2 4
 
2.0%
3 1
 
0.5%
4 159
77.6%
5 11
 
5.4%
6 24
 
11.7%
8 5
 
2.4%
12 1
 
0.5%
ValueCountFrequency (%)
12 1
 
0.5%
8 5
 
2.4%
6 24
 
11.7%
5 11
 
5.4%
4 159
77.6%
3 1
 
0.5%
2 4
 
2.0%

taille_moteur
Real number (ℝ)

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34080.702
Minimum16387
Maximum87539
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T14:09:00.167995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum16387
5-th percentile24171
Q126055
median32217
Q337870
95-th percentile54034.8
Maximum87539
Range71152
Interquartile range (IQR)11815

Descriptive statistics

Standard deviation11181.63
Coefficient of variation (CV)0.3280927
Kurtosis5.3071214
Mean34080.702
Median Absolute Deviation (MAD)6162
Skewness1.9480277
Sum6986544
Variance1.2502884 × 108
MonotonicityNot monotonic
2023-04-25T14:09:00.346851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
32758 15
 
7.3%
24712 15
 
7.3%
26055 14
 
6.8%
26318 14
 
6.8%
29005 13
 
6.3%
24171 12
 
5.9%
29546 12
 
5.9%
29267 8
 
3.9%
32217 7
 
3.4%
37870 7
 
3.4%
Other values (34) 88
42.9%
ValueCountFrequency (%)
16387 1
 
0.5%
18796 3
 
1.5%
21221 1
 
0.5%
21483 1
 
0.5%
24171 12
5.9%
24433 5
 
2.4%
24712 15
7.3%
26055 14
6.8%
26318 14
6.8%
27661 1
 
0.5%
ValueCountFrequency (%)
87539 1
 
0.5%
82705 1
 
0.5%
81640 1
 
0.5%
69284 2
 
1.0%
62844 2
 
1.0%
56125 3
1.5%
54520 1
 
0.5%
52094 3
1.5%
49145 4
2.0%
48604 6
2.9%
Distinct8
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
injection multipoint multipoint port unique
94 
carburateur 2 corps
66 
injection indirecte
20 
carburateur 1 corps
11 
injection D monopoint
 
9
Other values (3)
 
5

Length

Max length43
Median length30
Mean length30.146341
Min length19

Characters and Unicode

Total characters6180
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowinjection multipoint multipoint port unique
2nd rowinjection multipoint multipoint port unique
3rd rowinjection multipoint multipoint port unique
4th rowinjection multipoint multipoint port unique
5th rowinjection multipoint multipoint port unique

Common Values

ValueCountFrequency (%)
injection multipoint multipoint port unique 94
45.9%
carburateur 2 corps 66
32.2%
injection indirecte 20
 
9.8%
carburateur 1 corps 11
 
5.4%
injection D monopoint 9
 
4.4%
carburateur 4 corps 3
 
1.5%
injection carburant multipoint 1
 
0.5%
injection monopoint 1
 
0.5%

Length

2023-04-25T14:09:00.507118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T14:09:00.707197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
multipoint 189
24.2%
injection 125
16.0%
port 94
12.0%
unique 94
12.0%
carburateur 80
10.2%
corps 80
10.2%
2 66
 
8.4%
indirecte 20
 
2.6%
1 11
 
1.4%
monopoint 10
 
1.3%
Other values (3) 13
 
1.7%

Most occurring characters

ValueCountFrequency (%)
i 772
12.5%
t 708
11.5%
577
9.3%
n 574
9.3%
u 538
8.7%
o 518
8.4%
r 436
7.1%
p 373
 
6.0%
e 339
 
5.5%
c 306
 
5.0%
Other values (12) 1039
16.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5514
89.2%
Space Separator 577
 
9.3%
Decimal Number 80
 
1.3%
Uppercase Letter 9
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 772
14.0%
t 708
12.8%
n 574
10.4%
u 538
9.8%
o 518
9.4%
r 436
7.9%
p 373
6.8%
e 339
6.1%
c 306
 
5.5%
m 199
 
3.6%
Other values (7) 751
13.6%
Decimal Number
ValueCountFrequency (%)
2 66
82.5%
1 11
 
13.8%
4 3
 
3.8%
Space Separator
ValueCountFrequency (%)
577
100.0%
Uppercase Letter
ValueCountFrequency (%)
D 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5523
89.4%
Common 657
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 772
14.0%
t 708
12.8%
n 574
10.4%
u 538
9.7%
o 518
9.4%
r 436
7.9%
p 373
6.8%
e 339
6.1%
c 306
 
5.5%
m 199
 
3.6%
Other values (8) 760
13.8%
Common
ValueCountFrequency (%)
577
87.8%
2 66
 
10.0%
1 11
 
1.7%
4 3
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 772
12.5%
t 708
11.5%
577
9.3%
n 574
9.3%
u 538
8.7%
o 518
8.4%
r 436
7.1%
p 373
 
6.0%
e 339
 
5.5%
c 306
 
5.0%
Other values (12) 1039
16.8%

taux_alésage
Real number (ℝ)

Distinct38
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3297561
Minimum2.54
Maximum3.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T14:09:00.918162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.54
5-th percentile2.97
Q13.15
median3.31
Q33.58
95-th percentile3.78
Maximum3.94
Range1.4
Interquartile range (IQR)0.43

Descriptive statistics

Standard deviation0.27084371
Coefficient of variation (CV)0.081340404
Kurtosis-0.78504183
Mean3.3297561
Median Absolute Deviation (MAD)0.26
Skewness0.020156418
Sum682.6
Variance0.073356313
MonotonicityNot monotonic
2023-04-25T14:09:01.098887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
3.62 23
 
11.2%
3.19 20
 
9.8%
3.15 15
 
7.3%
3.03 12
 
5.9%
2.97 12
 
5.9%
3.46 9
 
4.4%
3.31 8
 
3.9%
3.43 8
 
3.9%
3.78 8
 
3.9%
3.27 7
 
3.4%
Other values (28) 83
40.5%
ValueCountFrequency (%)
2.54 1
 
0.5%
2.68 1
 
0.5%
2.91 7
3.4%
2.92 1
 
0.5%
2.97 12
5.9%
2.99 1
 
0.5%
3.01 5
2.4%
3.03 12
5.9%
3.05 6
2.9%
3.08 1
 
0.5%
ValueCountFrequency (%)
3.94 2
 
1.0%
3.8 2
 
1.0%
3.78 8
 
3.9%
3.76 1
 
0.5%
3.74 3
 
1.5%
3.7 5
 
2.4%
3.63 2
 
1.0%
3.62 23
11.2%
3.61 1
 
0.5%
3.6 1
 
0.5%

course
Real number (ℝ)

Distinct37
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2554146
Minimum2.07
Maximum4.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T14:09:01.297603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.07
5-th percentile2.64
Q13.11
median3.29
Q33.41
95-th percentile3.64
Maximum4.17
Range2.1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.31359701
Coefficient of variation (CV)0.096330898
Kurtosis2.1743964
Mean3.2554146
Median Absolute Deviation (MAD)0.14
Skewness-0.68970458
Sum667.36
Variance0.098343087
MonotonicityNot monotonic
2023-04-25T14:09:01.455260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
3.4 20
 
9.8%
3.23 14
 
6.8%
3.15 14
 
6.8%
3.03 14
 
6.8%
3.39 13
 
6.3%
2.64 11
 
5.4%
3.29 9
 
4.4%
3.35 9
 
4.4%
3.46 8
 
3.9%
3.11 6
 
2.9%
Other values (27) 87
42.4%
ValueCountFrequency (%)
2.07 1
 
0.5%
2.19 2
 
1.0%
2.36 1
 
0.5%
2.64 11
5.4%
2.68 2
 
1.0%
2.76 1
 
0.5%
2.8 2
 
1.0%
2.87 1
 
0.5%
2.9 3
 
1.5%
3.03 14
6.8%
ValueCountFrequency (%)
4.17 2
 
1.0%
3.9 3
 
1.5%
3.86 4
2.0%
3.64 5
2.4%
3.58 6
2.9%
3.54 4
2.0%
3.52 5
2.4%
3.5 6
2.9%
3.47 4
2.0%
3.46 8
3.9%

taux_compression
Real number (ℝ)

Distinct32
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.142537
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T14:09:01.611397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.6
median9
Q39.4
95-th percentile21.82
Maximum23
Range16
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation3.9720403
Coefficient of variation (CV)0.39162199
Kurtosis5.2330543
Mean10.142537
Median Absolute Deviation (MAD)0.4
Skewness2.6108625
Sum2079.22
Variance15.777104
MonotonicityNot monotonic
2023-04-25T14:09:01.773184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9 46
22.4%
9.4 26
12.7%
8.5 14
 
6.8%
9.5 13
 
6.3%
9.3 11
 
5.4%
8.7 9
 
4.4%
8 8
 
3.9%
9.2 8
 
3.9%
7 7
 
3.4%
8.6 5
 
2.4%
Other values (22) 58
28.3%
ValueCountFrequency (%)
7 7
3.4%
7.5 5
 
2.4%
7.6 4
 
2.0%
7.7 2
 
1.0%
7.8 1
 
0.5%
8 8
3.9%
8.1 2
 
1.0%
8.3 3
 
1.5%
8.4 5
 
2.4%
8.5 14
6.8%
ValueCountFrequency (%)
23 5
2.4%
22.7 1
 
0.5%
22.5 3
1.5%
22 1
 
0.5%
21.9 1
 
0.5%
21.5 4
2.0%
21 5
2.4%
11.5 1
 
0.5%
10.1 1
 
0.5%
10 3
1.5%

chevaux
Real number (ℝ)

Distinct59
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.11707
Minimum48
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T14:09:02.003764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q170
median95
Q3116
95-th percentile180.8
Maximum288
Range240
Interquartile range (IQR)46

Descriptive statistics

Standard deviation39.544167
Coefficient of variation (CV)0.37980483
Kurtosis2.6840062
Mean104.11707
Median Absolute Deviation (MAD)25
Skewness1.4053102
Sum21344
Variance1563.7411
MonotonicityNot monotonic
2023-04-25T14:09:02.181114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 19
 
9.3%
70 11
 
5.4%
69 10
 
4.9%
116 9
 
4.4%
110 8
 
3.9%
95 7
 
3.4%
114 6
 
2.9%
160 6
 
2.9%
101 6
 
2.9%
62 6
 
2.9%
Other values (49) 117
57.1%
ValueCountFrequency (%)
48 1
 
0.5%
52 2
 
1.0%
55 1
 
0.5%
56 2
 
1.0%
58 1
 
0.5%
60 1
 
0.5%
62 6
 
2.9%
64 1
 
0.5%
68 19
9.3%
69 10
4.9%
ValueCountFrequency (%)
288 1
 
0.5%
262 1
 
0.5%
207 3
1.5%
200 1
 
0.5%
184 2
1.0%
182 3
1.5%
176 2
1.0%
175 1
 
0.5%
162 2
1.0%
161 2
1.0%

tour_moteur
Real number (ℝ)

Distinct23
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5125.122
Minimum4150
Maximum6600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T14:09:02.333783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4250
Q14800
median5200
Q35500
95-th percentile5980
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation476.98564
Coefficient of variation (CV)0.093068155
Kurtosis0.086755856
Mean5125.122
Median Absolute Deviation (MAD)300
Skewness0.075158722
Sum1050650
Variance227515.3
MonotonicityNot monotonic
2023-04-25T14:09:02.475766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5500 37
18.0%
4800 36
17.6%
5000 27
13.2%
5200 23
11.2%
5400 13
 
6.3%
6000 9
 
4.4%
4500 7
 
3.4%
5800 7
 
3.4%
5250 7
 
3.4%
5100 5
 
2.4%
Other values (13) 34
16.6%
ValueCountFrequency (%)
4150 5
 
2.4%
4200 5
 
2.4%
4250 3
 
1.5%
4350 4
 
2.0%
4400 3
 
1.5%
4500 7
 
3.4%
4650 1
 
0.5%
4750 4
 
2.0%
4800 36
17.6%
4900 1
 
0.5%
ValueCountFrequency (%)
6600 2
 
1.0%
6000 9
 
4.4%
5900 3
 
1.5%
5800 7
 
3.4%
5750 1
 
0.5%
5600 1
 
0.5%
5500 37
18.0%
5400 13
 
6.3%
5300 1
 
0.5%
5250 7
 
3.4%

consommation_ville
Real number (ℝ)

Distinct29
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.217122
Minimum13
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T14:09:02.642936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q119
median24
Q330
95-th percentile36.98
Maximum49
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.5404353
Coefficient of variation (CV)0.25936486
Kurtosis0.58285645
Mean25.217122
Median Absolute Deviation (MAD)5
Skewness0.66437852
Sum5169.51
Variance42.777294
MonotonicityNot monotonic
2023-04-25T14:09:02.784463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
30.99 28
13.7%
19 27
13.2%
24 22
10.7%
27.01 14
 
6.8%
17 13
 
6.3%
25.99 12
 
5.9%
22.99 12
 
5.9%
21 8
 
3.9%
25 8
 
3.9%
30 8
 
3.9%
Other values (19) 53
25.9%
ValueCountFrequency (%)
13 1
 
0.5%
14 2
 
1.0%
15 3
 
1.5%
16 6
 
2.9%
17 13
6.3%
18 3
 
1.5%
19 27
13.2%
20 3
 
1.5%
21 8
 
3.9%
22 4
 
2.0%
ValueCountFrequency (%)
49 1
 
0.5%
47.04 1
 
0.5%
44.97 1
 
0.5%
38 7
3.4%
36.98 6
2.9%
36.02 1
 
0.5%
35 1
 
0.5%
33.99 1
 
0.5%
32.99 1
 
0.5%
32 1
 
0.5%

consommation_autoroute
Real number (ℝ)

Distinct30
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.74878
Minimum16
Maximum53.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T14:09:02.940496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q125
median30
Q333.99
95-th percentile42.8
Maximum53.95
Range37.95
Interquartile range (IQR)8.99

Descriptive statistics

Standard deviation6.886113
Coefficient of variation (CV)0.22394751
Kurtosis0.44139905
Mean30.74878
Median Absolute Deviation (MAD)5
Skewness0.54100275
Sum6303.5
Variance47.418552
MonotonicityNot monotonic
2023-04-25T14:09:03.081620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
25 19
 
9.3%
38 17
 
8.3%
24 17
 
8.3%
30 16
 
7.8%
32 16
 
7.8%
33.99 14
 
6.8%
36.98 13
 
6.3%
28 13
 
6.3%
29 10
 
4.9%
32.99 9
 
4.4%
Other values (20) 61
29.8%
ValueCountFrequency (%)
16 2
 
1.0%
17 1
 
0.5%
18 2
 
1.0%
19 2
 
1.0%
20 2
 
1.0%
22 8
3.9%
22.99 7
 
3.4%
24 17
8.3%
25 19
9.3%
25.99 3
 
1.5%
ValueCountFrequency (%)
53.95 1
 
0.5%
52.98 1
 
0.5%
50.05 1
 
0.5%
47.04 2
 
1.0%
46.03 2
 
1.0%
43 4
 
2.0%
42 3
 
1.5%
40.98 3
 
1.5%
39.01 2
 
1.0%
38 17
8.3%

prix
Real number (ℝ)

Distinct189
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13276.702
Minimum5118
Maximum45400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T14:09:03.260505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5118
5-th percentile6197
Q17788
median10295
Q316503
95-th percentile32472.4
Maximum45400
Range40282
Interquartile range (IQR)8715

Descriptive statistics

Standard deviation7988.849
Coefficient of variation (CV)0.60171937
Kurtosis3.0516511
Mean13276.702
Median Absolute Deviation (MAD)3306
Skewness1.777678
Sum2721724
Variance63821708
MonotonicityNot monotonic
2023-04-25T14:09:03.431113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8921 2
 
1.0%
9279 2
 
1.0%
7898 2
 
1.0%
8916 2
 
1.0%
7775 2
 
1.0%
8845 2
 
1.0%
7295 2
 
1.0%
7609 2
 
1.0%
6692 2
 
1.0%
6229 2
 
1.0%
Other values (179) 185
90.2%
ValueCountFrequency (%)
5118 1
0.5%
5151 1
0.5%
5195 1
0.5%
5348 1
0.5%
5389 1
0.5%
5399 1
0.5%
5499 1
0.5%
5572 2
1.0%
6095 1
0.5%
6189 1
0.5%
ValueCountFrequency (%)
45400 1
0.5%
41315 1
0.5%
40960 1
0.5%
37028 1
0.5%
36880 1
0.5%
36000 1
0.5%
35550 1
0.5%
35056 1
0.5%
34184 1
0.5%
34028 1
0.5%

marque
Categorical

Distinct22
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
toyota
32 
nissan
18 
mazda
17 
mitsubishi
13 
honda
13 
Other values (17)
112 

Length

Max length10
Median length9
Mean length6.204878
Min length3

Characters and Unicode

Total characters1272
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowalfa-romeo
2nd rowalfa-romeo
3rd rowalfa-romeo
4th rowaudi
5th rowaudi

Common Values

ValueCountFrequency (%)
toyota 32
15.6%
nissan 18
 
8.8%
mazda 17
 
8.3%
mitsubishi 13
 
6.3%
honda 13
 
6.3%
volkswagen 12
 
5.9%
subaru 12
 
5.9%
peugeot 11
 
5.4%
volvo 11
 
5.4%
dodge 9
 
4.4%
Other values (12) 57
27.8%

Length

2023-04-25T14:09:03.609568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
toyota 32
15.6%
nissan 18
 
8.8%
mazda 17
 
8.3%
mitsubishi 13
 
6.3%
honda 13
 
6.3%
volkswagen 12
 
5.9%
subaru 12
 
5.9%
peugeot 11
 
5.4%
volvo 11
 
5.4%
dodge 9
 
4.4%
Other values (12) 57
27.8%

Most occurring characters

ValueCountFrequency (%)
a 154
12.1%
o 152
11.9%
s 101
 
7.9%
t 100
 
7.9%
u 84
 
6.6%
i 76
 
6.0%
n 63
 
5.0%
e 60
 
4.7%
d 55
 
4.3%
m 49
 
3.9%
Other values (15) 378
29.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1269
99.8%
Dash Punctuation 3
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 154
12.1%
o 152
12.0%
s 101
 
8.0%
t 100
 
7.9%
u 84
 
6.6%
i 76
 
6.0%
n 63
 
5.0%
e 60
 
4.7%
d 55
 
4.3%
m 49
 
3.9%
Other values (14) 375
29.6%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1269
99.8%
Common 3
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 154
12.1%
o 152
12.0%
s 101
 
8.0%
t 100
 
7.9%
u 84
 
6.6%
i 76
 
6.0%
n 63
 
5.0%
e 60
 
4.7%
d 55
 
4.3%
m 49
 
3.9%
Other values (14) 375
29.6%
Common
ValueCountFrequency (%)
- 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1272
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 154
12.1%
o 152
11.9%
s 101
 
7.9%
t 100
 
7.9%
u 84
 
6.6%
i 76
 
6.0%
n 63
 
5.0%
e 60
 
4.7%
d 55
 
4.3%
m 49
 
3.9%
Other values (15) 378
29.7%

modele
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct141
Distinct (%)69.5%
Missing2
Missing (%)1.0%
Memory size1.7 KiB
504
 
6
corolla
 
6
corona
 
6
dl
 
4
civic
 
3
Other values (136)
178 

Length

Max length25
Median length18
Mean length7.0788177
Min length2

Characters and Unicode

Total characters1437
Distinct characters45
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique102 ?
Unique (%)50.2%

Sample

1st rowgiulia
2nd rowstelvio
3rd rowQuadrifoglio
4th row100 ls
5th row100ls

Common Values

ValueCountFrequency (%)
504 6
 
2.9%
corolla 6
 
2.9%
corona 6
 
2.9%
dl 4
 
2.0%
civic 3
 
1.5%
mark ii 3
 
1.5%
g4 3
 
1.5%
rabbit 3
 
1.5%
outlander 3
 
1.5%
mirage g4 3
 
1.5%
Other values (131) 163
79.5%

Length

2023-04-25T14:09:03.768298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
corolla 12
 
4.2%
sw 10
 
3.5%
corona 9
 
3.2%
glc 8
 
2.8%
civic 8
 
2.8%
custom 8
 
2.8%
504 7
 
2.5%
g4 6
 
2.1%
deluxe 5
 
1.8%
mirage 4
 
1.4%
Other values (141) 206
72.8%

Most occurring characters

ValueCountFrequency (%)
c 108
 
7.5%
a 107
 
7.4%
l 103
 
7.2%
r 100
 
7.0%
e 100
 
7.0%
o 93
 
6.5%
82
 
5.7%
i 71
 
4.9%
t 67
 
4.7%
s 54
 
3.8%
Other values (35) 552
38.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1128
78.5%
Decimal Number 179
 
12.5%
Space Separator 82
 
5.7%
Open Punctuation 13
 
0.9%
Close Punctuation 13
 
0.9%
Uppercase Letter 12
 
0.8%
Dash Punctuation 10
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 108
 
9.6%
a 107
 
9.5%
l 103
 
9.1%
r 100
 
8.9%
e 100
 
8.9%
o 93
 
8.2%
i 71
 
6.3%
t 67
 
5.9%
s 54
 
4.8%
u 41
 
3.6%
Other values (15) 284
25.2%
Decimal Number
ValueCountFrequency (%)
0 44
24.6%
4 37
20.7%
1 23
12.8%
2 21
11.7%
5 18
10.1%
9 12
 
6.7%
6 12
 
6.7%
3 10
 
5.6%
7 2
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
M 4
33.3%
D 3
25.0%
Q 1
 
8.3%
U 1
 
8.3%
X 1
 
8.3%
V 1
 
8.3%
C 1
 
8.3%
Space Separator
ValueCountFrequency (%)
82
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1140
79.3%
Common 297
 
20.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 108
 
9.5%
a 107
 
9.4%
l 103
 
9.0%
r 100
 
8.8%
e 100
 
8.8%
o 93
 
8.2%
i 71
 
6.2%
t 67
 
5.9%
s 54
 
4.7%
u 41
 
3.6%
Other values (22) 296
26.0%
Common
ValueCountFrequency (%)
82
27.6%
0 44
14.8%
4 37
12.5%
1 23
 
7.7%
2 21
 
7.1%
5 18
 
6.1%
( 13
 
4.4%
) 13
 
4.4%
9 12
 
4.0%
6 12
 
4.0%
Other values (3) 22
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1437
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 108
 
7.5%
a 107
 
7.4%
l 103
 
7.2%
r 100
 
7.0%
e 100
 
7.0%
o 93
 
6.5%
82
 
5.7%
i 71
 
4.9%
t 67
 
4.7%
s 54
 
3.8%
Other values (35) 552
38.4%

Interactions

2023-04-25T14:08:50.801900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:05.347398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:08.411285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:11.318548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-25T14:08:07.570561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:10.494259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:13.315060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:16.336632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:19.280518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:22.036218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:24.857857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:27.499671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:30.511513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:33.432832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:36.021601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:38.822324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:41.378467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:44.282527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:47.172364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:49.924877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:53.011955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:07.710155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:10.639573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:13.522039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:16.699382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:19.417556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:22.168725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:24.986951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:27.627270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:30.713305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:33.571879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:36.172888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:38.962576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:41.513869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:44.435863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:47.340841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:50.056657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:53.153659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:07.853282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:10.790867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:13.652720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:16.822218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:19.561425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:22.301387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:25.124583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:27.760062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:30.846191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:33.722121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:36.384851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:39.102128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:41.637967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:44.568947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:47.469663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:50.224966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:53.306078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:08.013728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:10.978062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:13.801885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:16.977504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:19.779276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:22.456181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:25.272850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:27.897743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:31.022561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:33.870308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:36.598485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:39.297642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:41.788215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:44.786457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:47.632932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:50.412868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:53.501752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:08.178019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:11.162215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:13.973059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:17.211552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:19.929767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:22.647072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:25.425907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:28.052150image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:31.173850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:34.044796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:36.765754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:39.514370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:41.930440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:44.933267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:47.799559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T14:08:50.652285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-25T14:09:03.947517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
car_IDetat_de_routeempattementlongueur_voiturelargeur_voiturehauteur_voiturepoids_vehiculenombre_cylindrestaille_moteurtaux_alésagecoursetaux_compressionchevauxtour_moteurconsommation_villeconsommation_autorouteprixcarburantturbonombre_portestype_vehiculeroues_motricesemplacement_moteurtype_moteursysteme_carburantmarque
car_ID1.000-0.1570.1960.1580.1490.2600.126-0.1190.0890.273-0.1600.1510.005-0.2300.0560.0210.0200.2890.2610.3430.1770.4240.3060.4120.3850.806
etat_de_route-0.1571.000-0.540-0.393-0.274-0.535-0.257-0.143-0.177-0.170-0.0190.023-0.0100.282-0.0180.053-0.1450.2170.1850.6840.3340.2660.2720.2220.2660.443
empattement0.196-0.5401.0000.9110.8230.6400.7660.3610.6490.5420.225-0.1310.507-0.315-0.494-0.5400.6800.3460.3100.4270.3370.3880.5680.3350.2210.499
longueur_voiture0.158-0.3930.9111.0000.8940.5350.8880.4660.7830.6380.185-0.1920.661-0.266-0.670-0.6980.8040.1100.2070.3650.2410.4090.0000.3170.3260.500
largeur_voiture0.149-0.2740.8230.8941.0000.3630.8720.4690.7750.6200.226-0.1530.692-0.206-0.689-0.7050.8170.2330.3010.3050.1280.4030.1600.3690.2460.527
hauteur_voiture0.260-0.5350.6400.5350.3631.0000.3550.0920.2060.226-0.023-0.0050.020-0.296-0.078-0.1440.2550.2770.2490.5380.4900.3640.2510.3860.2980.487
poids_vehicule0.126-0.2570.7660.8880.8720.3551.0000.5690.8780.7020.163-0.2170.808-0.236-0.812-0.8340.9100.3150.3720.2650.2390.4560.1030.3240.2970.493
nombre_cylindres-0.119-0.1430.3610.4660.4690.0920.5691.0000.6920.2110.068-0.1360.576-0.093-0.514-0.5090.5860.1700.2080.1510.0980.3300.2970.5540.3740.557
taille_moteur0.089-0.1770.6490.7830.7750.2060.8780.6921.0000.7010.292-0.2350.817-0.273-0.730-0.7210.8260.1570.2710.2070.2020.4690.6190.5270.3330.533
taux_alésage0.273-0.1700.5420.6380.6200.2260.7020.2110.7011.000-0.083-0.1600.639-0.298-0.609-0.6150.6440.1680.3350.1630.1510.4340.3270.4180.3450.533
course-0.160-0.0190.2250.1850.226-0.0230.1630.0680.292-0.0831.000-0.0700.130-0.074-0.030-0.0300.1110.3750.2650.1320.1510.3380.6150.4040.3030.581
taux_compression0.1510.023-0.131-0.192-0.153-0.005-0.217-0.136-0.235-0.160-0.0701.000-0.353-0.0220.4790.445-0.1740.9930.5540.1860.0480.1140.0000.3380.5180.493
chevaux0.005-0.0100.5070.6610.6920.0200.8080.5760.8170.6390.130-0.3531.0000.113-0.911-0.8860.8550.2190.3430.1710.1890.4020.8430.5140.3170.457
tour_moteur-0.2300.282-0.315-0.266-0.206-0.296-0.236-0.093-0.273-0.298-0.074-0.0220.1131.000-0.131-0.057-0.0660.5940.3110.2440.0740.2420.4480.3590.3630.470
consommation_ville0.056-0.018-0.494-0.670-0.689-0.078-0.812-0.514-0.730-0.609-0.0300.479-0.911-0.1311.0000.968-0.8290.3890.1860.0030.0000.3800.1100.2090.3040.360
consommation_autoroute0.0210.053-0.540-0.698-0.705-0.144-0.834-0.509-0.721-0.615-0.0300.445-0.886-0.0570.9681.000-0.8230.3360.3190.1190.0000.4370.1010.3250.3410.404
prix0.020-0.1450.6800.8040.8170.2550.9100.5860.8260.6440.111-0.1740.855-0.066-0.829-0.8231.0000.3380.4070.0000.2290.4510.4510.2880.2900.381
carburant0.2890.2170.3460.1100.2330.2770.3150.1700.1570.1680.3750.9930.2190.5940.3890.3360.3381.0000.3740.1610.1730.0880.0000.2500.9850.370
turbo0.2610.1850.3100.2070.3010.2490.3720.2080.2710.3350.2650.5540.3430.3110.1860.3190.4070.3741.0000.0000.0000.1180.0000.1500.6100.410
nombre_portes0.3430.6840.4270.3650.3050.5380.2650.1510.2070.1630.1320.1860.1710.2440.0030.1190.0000.1610.0001.0000.7410.0500.0670.2000.2450.298
type_vehicule0.1770.3340.3370.2410.1280.4900.2390.0980.2020.1510.1510.0480.1890.0740.0000.0000.2290.1730.0000.7411.0000.2140.4380.1320.1440.317
roues_motrices0.4240.2660.3880.4090.4030.3640.4560.3300.4690.4340.3380.1140.4020.2420.3800.4370.4510.0880.1180.0500.2141.0000.1240.4250.3870.603
emplacement_moteur0.3060.2720.5680.0000.1600.2510.1030.2970.6190.3270.6150.0000.8430.4480.1100.1010.4510.0000.0000.0670.4380.1241.0000.3990.0000.703
type_moteur0.4120.2220.3350.3170.3690.3860.3240.5540.5270.4180.4040.3380.5140.3590.2090.3250.2880.2500.1500.2000.1320.4250.3991.0000.3770.629
systeme_carburant0.3850.2660.2210.3260.2460.2980.2970.3740.3330.3450.3030.5180.3170.3630.3040.3410.2900.9850.6100.2450.1440.3870.0000.3771.0000.510
marque0.8060.4430.4990.5000.5270.4870.4930.5570.5330.5330.5810.4930.4570.4700.3600.4040.3810.3700.4100.2980.3170.6030.7030.6290.5101.000

Missing values

2023-04-25T14:08:53.815674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-25T14:08:54.350969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

car_IDetat_de_routecarburantturbonombre_portestype_vehiculeroues_motricesemplacement_moteurempattementlongueur_voiturelargeur_voiturehauteur_voiturepoids_vehiculetype_moteurnombre_cylindrestaille_moteursysteme_carburanttaux_alésagecoursetaux_compressionchevauxtour_moteurconsommation_villeconsommation_autorouteprixmarquemodele
013gasstandard2decapotablearrieredevant2.254.291.631.241.27double ACT434904.0injection multipoint multipoint port unique3.472.689.0111500021.027.0113495alfa-romeogiulia
123gasstandard2decapotablearrieredevant2.254.291.631.241.27double ACT434904.0injection multipoint multipoint port unique3.472.689.0111500021.027.0116500alfa-romeostelvio
231gasstandard2hayonarrieredevant2.404.351.661.331.41soupapes en tête à manchon640820.0injection multipoint multipoint port unique2.683.479.0154500019.025.9916500alfa-romeoQuadrifoglio
342gasstandard4berlineavantdevant2.534.491.681.381.17ACT429267.0injection multipoint multipoint port unique3.193.4010.0102550024.030.0013950audi100 ls
452gasstandard4berline4motricedevant2.524.491.691.381.41ACT536527.0injection multipoint multipoint port unique3.193.408.0115550018.022.0017450audi100ls
562gasstandard2berlineavantdevant2.534.501.681.351.25ACT536527.0injection multipoint multipoint port unique3.193.408.5110550019.025.0015250audifox
671gasstandard4berlineavantdevant2.694.891.811.411.42ACT536527.0injection multipoint multipoint port unique3.193.408.5110550019.025.0017710audi100ls
781gasstandard4breakavantdevant2.694.891.811.411.48ACT536527.0injection multipoint multipoint port unique3.193.408.5110550019.025.0018920audi5000
891gasturbo4berlineavantdevant2.694.891.811.421.54ACT535183.0injection multipoint multipoint port unique3.133.408.3140550017.020.0023875audi4000
9100gasturbo2hayon4motricedevant2.534.531.721.321.53ACT535183.0injection multipoint multipoint port unique3.133.407.0160550016.022.0017859audi5000s (diesel)
car_IDetat_de_routecarburantturbonombre_portestype_vehiculeroues_motricesemplacement_moteurempattementlongueur_voiturelargeur_voiturehauteur_voiturepoids_vehiculetype_moteurnombre_cylindrestaille_moteursysteme_carburanttaux_alésagecoursetaux_compressionchevauxtour_moteurconsommation_villeconsommation_autorouteprixmarquemodele
195196-1gasstandard4breakarrieredevant2.654.81.711.461.52ACT437870.0injection multipoint multipoint port unique3.783.159.5114540022.9928.0013415volvo144ea
196197-2gasstandard4berlinearrieredevant2.654.81.711.431.47ACT437870.0injection multipoint multipoint port unique3.783.159.5114540024.0028.0015985volvo244dl
197198-1gasstandard4breakarrieredevant2.654.81.711.461.52ACT437870.0injection multipoint multipoint port unique3.783.159.5114540024.0028.0016515volvo245
198199-2gasturbo4berlinearrieredevant2.654.81.711.431.52ACT434904.0injection multipoint multipoint port unique3.623.157.5162510017.0022.0018420volvo264gl
199200-1gasturbo4breakarrieredevant2.654.81.711.461.58ACT434904.0injection multipoint multipoint port unique3.623.157.5162510017.0022.0018950volvodiesel
200201-1gasstandard4berlinearrieredevant2.774.81.751.411.48ACT437870.0injection multipoint multipoint port unique3.783.159.5114540022.9928.0016845volvo145e (sw)
201202-1gasturbo4berlinearrieredevant2.774.81.751.411.52ACT437870.0injection multipoint multipoint port unique3.783.158.7160530019.0025.0019045volvo144ea
202203-1gasstandard4berlinearrieredevant2.774.81.751.411.51soupapes en tête à manchon646457.0injection multipoint multipoint port unique3.582.878.8134550018.0022.9921485volvo244dl
203204-1dieselturbo4berlinearrieredevant2.774.81.751.411.61ACT638936.0injection indirecte3.013.4023.0106480025.9927.0122470volvo246
204205-1gasturbo4berlinearrieredevant2.774.81.751.411.53ACT437870.0injection multipoint multipoint port unique3.783.159.5114540019.0025.0022625volvo264gl